OpenRaiser/Intern-Atlas huggingface dataset:
Histogram-Equalized Quantization for logic-gated Residual Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

Adjusting the quantization according to the data or to the model loss seems mandatory to enable a high accuracy in the context of quantized neural networks. This work presents Histogram-Equalized Quantization (HEQ), an adaptive framework for linear symmetric quantization. HEQ automatically adapts the quantization thresholds using a unique step size optimization. We empirically show that HEQ achieves state-of-the-art performances on CIFAR-10. Experiments on the STL-10 dataset even show that HEQ enables a proper training of our proposed logic-gated (OR, MUX) residual networks with a higher accuracy at a lower hardware complexity than previous work.
Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training
Venue: arXiv cs.LG 2025
Year: 2025

Adversarial training has proven to be a highly effective method for improving the robustness of deep neural networks against adversarial attacks. Nonetheless, it has been observed to exhibit a limitation in terms of robust fairness, characterized by a significant disparity in robustness across different classes. Recent efforts to mitigate this problem have turned to class-wise reweighted methods. However, these methods suffer from a lack of rigorous theoretical analysis and are limited in their exploration of the weight space, as they mainly rely on existing heuristic algorithms or intuition to compute weights. In addition, these methods fail to guarantee the consistency of the optimization direction due to the decoupled optimization of weights and the model parameters. They potentially lead to suboptimal weight assignments and consequently, a suboptimal model. To address these problems, this paper proposes a novel min-max training framework, Class Optimal Distribution Adversarial Training (CODAT), which employs distributionally robust optimization to fully explore the class-wise weight space, thus enabling the identification of the optimal weight with theoretical guarantees. Furthermore, we derive a closed-form optimal solution to the internal maximization and then get a deterministic equivalent objective function, which provides a theoretical basis for the joint optimization of weights and model parameters. Meanwhile, we propose a fairness elasticity coefficient for the evaluation of the algorithm with regard to both robustness and robust fairness. Experimental results on various datasets show that the proposed method can effectively improve the robust fairness of the model and outperform the state-of-the-art approaches.
OpenOmni: Advancing Open-Source Omnimodal Large Language Models with Progressive Multimodal Alignment and Real-Time Self-Aware Emotional Speech Synthesis
Venue: arXiv cs.CL 2025
Year: 2025

Recent advancements in omnimodal learning have significantly improved understanding and generation across images, text, and speech, yet these developments remain predominantly confined to proprietary models. The lack of high-quality omnimodal datasets and the challenges of real-time emotional speech synthesis have notably hindered progress in open-source research. To address these limitations, we introduce \name, a two-stage training framework that integrates omnimodal alignment and speech generation to develop a state-of-the-art omnimodal large language model. In the alignment phase, a pre-trained speech model undergoes further training on text-image tasks, enabling (near) zero-shot generalization from vision to speech, outperforming models trained on tri-modal datasets. In the speech generation phase, a lightweight decoder is trained on speech tasks with direct preference optimization, enabling real-time emotional speech synthesis with high fidelity. Experiments show that \name surpasses state-of-the-art models across omnimodal, vision-language, and speech-language benchmarks. It achieves a 4-point absolute improvement on OmniBench over the leading open-source model VITA, despite using 5x fewer training samples and a smaller model size (7B vs. 7x8B). Additionally, \name achieves real-time speech generation with <1s latency at non-autoregressive mode, reducing inference time by 5x compared to autoregressive methods, and improves emotion classification accuracy by 7.7\%
Large-Scale Spectral Graph Neural Networks via Laplacian Sparsification: Technical Report
Venue: arXiv cs.LG 2025
Year: 2025

Graph Neural Networks (GNNs) play a pivotal role in graph-based tasks for their proficiency in representation learning. Among the various GNN methods, spectral GNNs employing polynomial filters have shown promising performance on tasks involving both homophilous and heterophilous graph structures. However, The scalability of spectral GNNs on large graphs is limited because they learn the polynomial coefficients through multiple forward propagation executions during forward propagation. Existing works have attempted to scale up spectral GNNs by eliminating the linear layers on the input node features, a change that can disrupt end-to-end training, potentially impact performance, and become impractical with high-dimensional input features. To address the above challenges, we propose "Spectral Graph Neural Networks with Laplacian Sparsification (SGNN-LS)", a novel graph spectral sparsification method to approximate the propagation patterns of spectral GNNs. We prove that our proposed method generates Laplacian sparsifiers that can approximate both fixed and learnable polynomial filters with theoretical guarantees. Our method allows the application of linear layers on the input node features, enabling end-to-end training as well as the handling of raw text features. We conduct an extensive experimental analysis on datasets spanning various graph scales and properties to demonstrate the superior efficiency and effectiveness of our method. The results show that our method yields superior results in comparison with the corresponding approximated base models, especially on dataset Ogbn-papers100M(111M nodes, 1.6B edges) and MAG-scholar-C (2.8M features).
Analysis of Climatic Trends and Variability in Indian Topography
Venue: arXiv cs.SI 2025
Year: 2025

The climatic change is one of the serious concerns nowadays. The impacts of climate change are global in scope and unprecedented in scale. Moreover, a small perturbation in climatic changes affects not only the pristine ecosystem but also the socioeconomic sectors. Specifically, the affect of climatic changes is related to frequent casualties. This makes it essential to dwelve deeper into analyzing the socio-climatic trends and variability. This work provides a comprehensive analysis of India's climatic trends, emphasizing on regional variations and specifically delving into the unique climate of Delhi. Specifically, this research unveils the temporal and spatial variations in temperature patterns by amalgamating extensive datasets encompassing India's diverse landscapes. The study uses advanced statistical tools and methodologies to scrutinize temperature's annual and seasonal variability. The insights drawn from this rigorous analysis may offer invaluable contributions to regional planning strategies, adaptive measures, and informed decision-making amidst the complex impacts of climate change. By bridging the gap between broader climatic trends and localized impacts, this research aims to facilitate more effective measures to mitigate and adapt to the multifaceted challenges of climate change, ensuring a more nuanced and tailored approaches. We utilized the Mann-Kendall test and Theil-Sen's slope estimator to analyze the trends and variability of the climatic conditions over the decades. The results demonstrate that temperature variations have increased over 0.58oC on average over the last decade. Moreover, over last decade the variability of Indian states shows that Lakshadweep faced the highest change (0.87oC), highlighting coastal vulnerability, while Tripura observed the least change of 0.07oC.
Goldilocks Isolation: High Performance VMs with Edera
Venue: arXiv cs.CR 2025
Year: 2025

Organizations run applications on cloud infrastructure shared between multiple users and organizations. Popular tooling for this shared infrastructure, including Docker and Kubernetes, supports such multi-tenancy through the use of operating system virtualization. With operating system virtualization (known as containerization), multiple applications share the same kernel, reducing the runtime overhead. However, this shared kernel presents a large attack surface and has led to a proliferation of container escape attacks in which a kernel exploit lets an attacker escape the isolation of operating system virtualization to access other applications or the operating system itself. To address this, some systems have proposed a return to hypervisor virtualization for stronger isolation between applications. However, no existing system has achieved both the isolation of hypervisor virtualization and the performance and usability of operating system virtualization. We present Edera, an optimized type 1 hypervisor that uses paravirtualization to improve the runtime of hypervisor virtualization. We illustrate Edera's usability and performance through two use cases. First, we create a container runtime compatible with Kubernetes that runs on the Edera hypervisor. This implementation can be used as a drop-in replacement for the Kubernetes runtime and is compatible with all the tooling in the Kubernetes ecosystem. Second, we use Edera to provide driver isolation for hardware drivers, including those for networking, storage, and GPUs. This use of isolation protects the hypervisor and other applications from driver vulnerabilities. We find that Edera has runtime comparable to Docker with .9% slower cpu speeds, an average of 3% faster system call performance, and memory performance 0-7% faster. It achieves this with a 648 millisecond increase in startup time from Docker's 177.4 milliseconds.
Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity
Venue: arXiv cs.LG 2025
Year: 2025

Federated- and Continual Learning have been established as approaches to enable privacy-aware learning on continuously changing data, as required for deploying AI systems in histopathology images. However, data shifts can occur in a dynamic world, spatially between institutions and temporally, due to changing data over time. This leads to two issues: Client Drift, where the central model degrades from aggregating data from clients trained on shifted data, and Catastrophic Forgetting, from temporal shifts such as changes in patient populations. Both tend to degrade the model's performance of previously seen data or spatially distributed training. Despite both problems arising from the same underlying problem of data shifts, existing research addresses them only individually. In this work, we introduce a method that can jointly alleviate Client Drift and Catastrophic Forgetting by using our proposed Dynamic Barlow Continuity that evaluates client updates on a public reference dataset and uses this to guide the training process to a spatially and temporally shift-invariant model. We evaluate our approach on the histopathology datasets BCSS and Semicol and prove our method to be highly effective by jointly improving the dice score as much as from 15.8% to 71.6% in Client Drift and from 42.5% to 62.8% in Catastrophic Forgetting. This enables Dynamic Learning by establishing spatio-temporal shift-invariance.
FrontierNet: Learning Visual Cues to Explore
Venue: arXiv cs.RO 2025
Year: 2025

Exploration of unknown environments is crucial for autonomous robots; it allows them to actively reason and decide on what new data to acquire for different tasks, such as mapping, object discovery, and environmental assessment. Existing solutions, such as frontier-based exploration approaches, rely heavily on 3D map operations, which are limited by map quality and, more critically, often overlook valuable context from visual cues. This work aims at leveraging 2D visual cues for efficient autonomous exploration, addressing the limitations of extracting goal poses from a 3D map. We propose a visual-only frontier-based exploration system, with FrontierNet as its core component. FrontierNet is a learning-based model that (i) proposes frontiers, and (ii) predicts their information gain, from posed RGB images enhanced by monocular depth priors. Our approach provides an alternative to existing 3D-dependent goal-extraction approaches, achieving a 15\% improvement in early-stage exploration efficiency, as validated through extensive simulations and real-world experiments. The project is available at https://github.com/cvg/FrontierNet.
Resilient Peer-to-peer Learning based on Adaptive Aggregation
Venue: arXiv cs.LG 2025
Year: 2025

Collaborative learning in peer-to-peer networks offers the benefits of distributed learning while mitigating the risks associated with single points of failure inherent in centralized servers. However, adversarial workers pose potential threats by attempting to inject malicious information into the network. Thus, ensuring the resilience of peer-to-peer learning emerges as a pivotal research objective. The challenge is exacerbated in the presence of non-convex loss functions and non-iid data distributions. This paper introduces a resilient aggregation technique tailored for such scenarios, aimed at fostering similarity among peers' learning processes. The aggregation weights are determined through an optimization procedure, and use the loss function computed using the neighbor's models and individual private data, thereby addressing concerns regarding data privacy in distributed machine learning. Theoretical analysis demonstrates convergence of parameters with non-convex loss functions and non-iid data distributions. Empirical evaluations across three distinct machine learning tasks support the claims. The empirical findings, which encompass a range of diverse attack models, also demonstrate improved accuracy when compared to existing methodologies.
XGeM: A Multi-Prompt Foundation Model for Multimodal Medical Data Generation
Venue: arXiv cs.AI 2025
Year: 2025

The adoption of Artificial Intelligence in medical imaging holds great promise, yet it remains hindered by challenges such as data scarcity, privacy concerns, and the need for robust multimodal integration. While recent advances in generative modeling have enabled high-quality synthetic data generation, existing approaches are often limited to unimodal, unidirectional synthesis and therefore lack the ability to jointly synthesize multiple modalities while preserving clinical consistency. To address this challenge, we introduce XGeM, a 6.77-billion-parameter multimodal generative model designed to support flexible, any-to-any synthesis between medical data modalities. XGeM constructs a shared latent space via contrastive learning and introduces a novel Multi-Prompt Training strategy, enabling conditioning on arbitrary subsets of input modalities. This design allows the model to adapt to heterogeneous clinical inputs and generate multiple outputs jointly, preserving both semantic and structural coherence. We extensively validate XGeM: first we benchmark it against five competitors on the MIMIC-CXR dataset, a state-of-the-art dataset for multi-view Chest X-ray and radiological report generation. Secondly, we perform a Visual Turing Test with expert radiologists to assess the realism and clinical relevance of the generated data, ensuring alignment with real-world scenarios. Finally, we show how XGeM can support key medical data challenges such as anonymization, class imbalance, and data scarcity, underscoring its utility as a foundation model for medical data synthesis. Project page is at https://cosbidev.github.io/XGeM/.
Optimal Trading of a Charging-Station Company in Auction Markets for Electricity
Venue: arXiv cs.DM 2025
Year: 2025

This paper addresses a charging-station company (Chargco) for electric and hydrogen vehicles. The optimal trading of the Chargco in day-ahead and intraday auction markets for electricity is modeled as a stochastic Mixed-Integer Quadratic Program (MIQP). We propose a series of linearization and reformulation techniques to reformulate the stochastic MIQP as a mixed-integer linear program (MILP). To model stochasticity, we utilize generative adversarial networks to cluster electricity market price scenarios. Additionally, a combination of random forests and linear regression is employed to model the relationship between Chargco electricity and hydrogen loads and their selling prices. Finally, we propose an Improved L-Shaped Decomposition (ILSD) algorithm to solve our stochastic MILP. Our ILSD algorithm not only addresses infeasibilities through an innovative approach but also incorporates warm starts, valid inequalities and multiple generation cuts, thereby reducing computational complexity. Numerical experiments illustrate the Chargco trading using our proposed stochastic MILP and its solution algorithm.
Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought
Venue: arXiv cs.AI 2025
Year: 2025

We propose a novel framework, Meta Chain-of-Thought (Meta-CoT), which extends traditional Chain-of-Thought (CoT) by explicitly modeling the underlying reasoning required to arrive at a particular CoT. We present empirical evidence from state-of-the-art models exhibiting behaviors consistent with in-context search, and explore methods for producing Meta-CoT via process supervision, synthetic data generation, and search algorithms. Finally, we outline a concrete pipeline for training a model to produce Meta-CoTs, incorporating instruction tuning with linearized search traces and reinforcement learning post-training. Finally, we discuss open research questions, including scaling laws, verifier roles, and the potential for discovering novel reasoning algorithms. This work provides a theoretical and practical roadmap to enable Meta-CoT in LLMs, paving the way for more powerful and human-like reasoning in artificial intelligence.
Re-ranking the Context for Multimodal Retrieval Augmented Generation
Venue: arXiv cs.LG 2025
Year: 2025

Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating external knowledge to generate a response within a context with improved accuracy and reduced hallucinations. However, multi-modal RAG systems face unique challenges: (i) the retrieval process may select irrelevant entries to user query (e.g., images, documents), and (ii) vision-language models or multi-modal language models like GPT-4o may hallucinate when processing these entries to generate RAG output. In this paper, we aim to address the first challenge, i.e, improving the selection of relevant context from the knowledge-base in retrieval phase of the multi-modal RAG. Specifically, we leverage the relevancy score (RS) measure designed in our previous work for evaluating the RAG performance to select more relevant entries in retrieval process. The retrieval based on embeddings, say CLIP-based embedding, and cosine similarity usually perform poorly particularly for multi-modal data. We show that by using a more advanced relevancy measure, one can enhance the retrieval process by selecting more relevant pieces from the knowledge-base and eliminate the irrelevant pieces from the context by adaptively selecting up-to-$k$ entries instead of fixed number of entries. Our evaluation using COCO dataset demonstrates significant enhancement in selecting relevant context and accuracy of the generated response.
Discovering new robust local search algorithms with neuro-evolution
Venue: arXiv cs.NE 2025
Year: 2025

This paper explores a novel approach aimed at overcoming existing challenges in the realm of local search algorithms. Our aim is to improve the decision process that takes place within a local search algorithm so as to make the best possible transitions in the neighborhood at each iteration. To improve this process, we propose to use a neural network that has the same input information as conventional local search algorithms. In this paper, which is an extension of the work presented at EvoCOP2024, we investigate different ways of representing this information so as to make the algorithm as efficient as possible but also robust to monotonic transformations of the problem objective function. To assess the efficiency of this approach, we develop an experimental setup centered around NK landscape problems, offering the flexibility to adjust problem size and ruggedness. This approach offers a promising avenue for the emergence of new local search algorithms and the improvement of their problem-solving capabilities for black-box problems. The last version of this article is published in the journal SN Computer Science (Springer).
Efficient and Responsible Adaptation of Large Language Models for Robust and Equitable Top-k Recommendations
Venue: arXiv cs.IR 2025
Year: 2025

Conventional recommendation systems (RSs) are typically optimized to enhance performance metrics uniformly across all training samples, inadvertently overlooking the needs of diverse user populations. The performance disparity among various populations can harm the model's robustness to sub-populations due to the varying user properties. While large language models (LLMs) show promise in enhancing RS performance, their practical applicability is hindered by high costs, inference latency, and degraded performance on long user queries. To address these challenges, we propose a hybrid task allocation framework designed to promote social good by equitably serving all user groups. By adopting a two-phase approach, we promote a strategic assignment of tasks for efficient and responsible adaptation of LLMs. Our strategy works by first identifying the weak and inactive users that receive a suboptimal ranking performance by RSs. Next, we use an in-context learning approach for such users, wherein each user interaction history is contextualized as a distinct ranking task. We evaluate our hybrid framework by incorporating eight different recommendation algorithms and three different LLMs -- both open and close-sourced. Our results on three real-world datasets show a significant reduction in weak users and improved robustness to subpopulations without disproportionately escalating costs.
Decoding rank metric Reed-Muller codes
Venue: arXiv cs.IT 2025
Year: 2025

In this article, we investigate the decoding of the rank metric Reed--Muller codes introduced by Augot, Couvreur, Lavauzelle and Neri in 2021. These codes are defined from Abelian Galois extensions extending the construction of Gabidulin codes over arbitrary cyclic Galois extensions. We propose a polynomial time algorithm that rests on the structure of Dickson matrices, works on any such code and corrects any error of rank up to half the minimum distance.